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PyTorch Deep Learning Hands-On

You're reading from   PyTorch Deep Learning Hands-On Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788834131
Length 250 pages
Edition 1st Edition
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Authors (2):
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Sherin Thomas Sherin Thomas
Author Profile Icon Sherin Thomas
Sherin Thomas
Sudhanshu Passi Sudhanshu Passi
Author Profile Icon Sudhanshu Passi
Sudhanshu Passi
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Toc

Ideation and planning

Usually, in an organization the product team shows up with a problem statement for the engineering team, wanting to know whether they can solve it or not. This is the start of the ideation phase. In academia, this could be the decision phase, where candidates have to find a problem for their thesis. In the ideation phase, the engineers brainstorm and find the theoretical implementations that could potentially solve the problem. Along with converting the problem statement to a theoretical solution, the ideation phase is where we decide what the data types are and what dataset we should use to build the proof of concept (POC) of the minimum viable product (MVP). This is the stage where the team decides which framework to go with by analyzing the behavior of the problem statement, existing available implementations, available pretrained models, and so on.

This stage is very common in the industry and I have thousands of examples where a well-planned ideation phase...

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